In [31]:
!pip install folium
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import folium
from folium.plugins import HeatMap
from folium.plugins import HeatMapWithTime
import datetime
import statsmodels.formula.api as sm
In [32]:
original = pd.read_excel('traffic_sample.xlsx')
In [33]:
# Converting the Latitude and Longitude Attributes to a Float
original["Latitude"] = original["Latitude"].astype(float)
original["Longitude"] = original["Longitude"].astype(float)
original = original[original["Gender"] != "U"]
original = original[(original["Year"] != 0) & (original["Year"] < 2020) & (original["Year"] > 1900)]
In [34]:
filtered_cols = ["Date Of Stop", "Time Of Stop", "SubAgency",
"Description", "Location", "Latitude", "Longitude",
"Accident", "Belts", "Personal Injury", "Property Damage",
"Fatal", "Alcohol", "Work Zone", "VehicleType",
"Year", "Make", "Violation Type", "Contributed To Accident",
"Race", "Gender", "DL State"]
# Can break up the criteria above to make the dataframe more tidy
sam = original[filtered_cols].copy()
In [35]:
def generate_map(loc = [39.1247, -77.1905], zoom = 10.5, tile = "openstreetmap"):
res_map = folium.Map(location = loc, zoom_start = zoom, control_scale = True, tiles = tile)
# Add the Tile (or Style) of the Map
folium.TileLayer('openstreetmap').add_to(res_map)
folium.TileLayer('Stamen Watercolor').add_to(res_map)
folium.TileLayer('Stamen Toner').add_to(res_map)
return res_map
In [36]:
# This Function returns the designated color assigned to a race.
def color_select(race):
ethnicity = {'ASIAN': "#ed8134", # Orange
'BLACK': "#391cba", #Indigo
'HISPANIC': "#119992", #Teal
'NATIVE AMERICAN': "#9412b8", # Violet
'OTHER': "#127bb8", # Blue
'WHITE': "#e81c1c"} # Red
return ethnicity[race]
Map Exploring the Race and Gender¶
In [37]:
# Creating an Empty Map
map_total = generate_map()
# other mapping code (e.g. lines, markers etc.)
asian_fg = folium.FeatureGroup(name = "Asian")
black_fg = folium.FeatureGroup(name = "Black")
his_fg = folium.FeatureGroup(name = "Hispanic")
na_fg = folium.FeatureGroup(name = "Native American")
other_fg = folium.FeatureGroup(name = "Other")
white_fg = folium.FeatureGroup(name = "White")
race = {'ASIAN': asian_fg,
'BLACK': black_fg,
'HISPANIC': his_fg,
'NATIVE AMERICAN': na_fg,
'OTHER': other_fg,
'WHITE': white_fg}
legend_html = '''
<style>
.circle {
height: 10px;
width: 10px;
background-color: orange;
border-radius: 50%;
}
.square {
height: 10px;
width: 10px;
background-color: #ed8134;
}
div {
display: inline-block;
}
legend {
font-size: 13px
}
.triangle {
width: 0;
height: 0;
border-left: 7.5px solid transparent;
border-right: 7.5px solid transparent;
border-bottom: 15px solid #ed8134;
}
</style>
<div style="position: fixed;
left: 50px; width: 150px;
border:2px solid black; z-index:9999; font-size:12px; background-color: white;">
<legend><b>Legend:</b></legend>
<b>Race: </b><br>
Asian: <div class = circle style = "background-color: #ed8134"> </div> <br>
White: <div class = circle style = "background-color: #e81c1c"> </div><br>
Black: <div class = circle style = "background-color: #391cba"> </div><br>
Hispanic: <div class = circle style = "background-color: #119992"> </div><br>
Native American: <div class = circle style = "background-color: #9412b8"> </div><br>
Other: <div class = circle style = "background-color: #127bb8"> </div>
<hr>
<b>Gender: </b><br>
Male: <div class = triangle> </div> <br>
Female: <div class = square> </div> <br>
</div>
'''
map_total.get_root().html.add_child(folium.Element(legend_html))
for ind, row in sam.iterrows():
entry = (folium.RegularPolygonMarker(location = [row["Latitude"],row["Longitude"]], popup = row["Description"],
color= color_select(row["Race"]), fill = True, weight = 1,
number_of_sides = 3 if row["Gender"] == "M" else 6,
radius = 4, opactity = .4))
entry.add_to(race[row["Race"]])
for r in race:
race[r].add_to(map_total)
folium.LayerControl().add_to(map_total)
map_total
Out[37]:
Bar Graph Relating Gender, Race, and the number of Traffic Violation¶
In [38]:
gr_df = sam.copy()
gr_df["count"] = 1
aggregation_functions = {'count': 'sum'}
nd = gr_df.groupby(['Gender', 'Race']).aggregate(aggregation_functions)
# Setting up the plot and dimension
fig, axs = plt.subplots()
fig.set_figheight(30)
fig.set_figwidth(40)
b1 = sns.barplot(x="Gender", y ="count", hue="Race", palette = "Spectral", data=nd.reset_index(), ax = axs)
b1.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),
fancybox=True, shadow=True, ncol=3, labelspacing=2, fontsize = 20)
b1.set_title("The Occurrence of Traffic Violation Based on Gender and Race", fontsize = 40)
b1.set_ylabel("Count", fontsize = 30)
b1.set_xlabel("Gender", fontsize = 30)
b1.tick_params(axis='both', labelsize=25)
plt.show()
HeatMap Exploring the Occurrence based on Time (Hours)¶
In [39]:
sam["hour"] = [t.hour for t in sam["Time Of Stop"]]
cut = pd.cut(sam["hour"], bins = [0,2,4,6,8,10,12,14,16,18,20,22,24],
labels = [1,2,3,4,5,6,7,8,9,10,11,12], right = False)
sam["cut"] = cut
In [40]:
df_copy = sam.copy()
df_copy['count'] = 1
hr_map = generate_map()
hm_fg = []
hr = 0
for ind in range(12):
temp_name = "Hours " + str(hr) + " to " + str(hr + 1)
hm_fg.append(folium.FeatureGroup(name = temp_name, show= True if ind == 0 else False))
hr += 2
# Group time together to have more during a specifc set of hours\
for index in range(12):
temp = df_copy[df_copy["cut"] == index + 1]
HeatMap(data=temp[['Latitude', 'Longitude', 'count']]
.groupby(['Latitude', 'Longitude', 'count'])
.sum()
.reset_index()
.values.tolist(),
radius=8, max_zoom=13).add_to(hm_fg[index])
for fg in hm_fg:
fg.add_to(hr_map)
folium.LayerControl().add_to(hr_map)
hr_map
Out[40]:
HeatMap Over Time¶
In [41]:
time_map = generate_map()
df_hour_list = []
for hour in df_copy["cut"].sort_values().unique():
df_hour_list.append(df_copy.loc[df_copy.hour == hour, ['Latitude', 'Longitude', 'count']]
.groupby(['Latitude', 'Longitude']).sum().reset_index().values.tolist())
HeatMapWithTime(df_hour_list, radius=8, gradient={0.2: 'blue', 0.4: 'lime', 0.6: 'orange', 1: 'red'},
min_opacity=0.5, max_opacity=0.8, use_local_extrema=True, auto_play=True).add_to(time_map)
folium.LayerControl().add_to(time_map)
time_map
Out[41]:
Total HeatMap¶
In [42]:
df_copy = sam.copy()
df_copy['count'] = 1
base_map = generate_map()
HeatMap(data=df_copy[['Latitude', 'Longitude', 'count']]
.groupby(['Latitude', 'Longitude', 'count'])
.sum()
.reset_index()
.values.tolist(),
radius=8, max_zoom=13).add_to(base_map)
folium.LayerControl().add_to(base_map)
base_map
Out[42]:
Exploring the Vehicle Type and Year¶
In [43]:
##### VehicleType, Year, Make
vy_df = sam.copy()
vy_df['count'] = 1
vy_df = vy_df.sort_values("Year")
aggregation_functions = {'count': 'sum'}
nd2 = vy_df.groupby(['Year', 'VehicleType']).aggregate(aggregation_functions)
# Setting up the plot and dimension
fig, axs = plt.subplots()
fig.set_figheight(30)
fig.set_figwidth(40)
l = sns.lineplot(x = "Year", y = "count", hue = "VehicleType",data = nd2.reset_index(), ax = axs)
l.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),
fancybox=True, shadow=True, ncol=10, labelspacing=2, fontsize = 18)
l.set_title("The Occurrence of Traffic Violation Based on the Vehicle Type and Year", fontsize = 40)
l.set_ylabel("Occurrence", fontsize = 30)
l.set_xlabel("Year", fontsize = 30)
l.tick_params(axis='both', labelsize=25)
plt.show()
Can We Predict Where A Violation Might Occur?¶
In [44]:
district = np.unique(original["SubAgency"])
simp_district = {}
for dist in district:
if dist[0] == "H":
simp_district[dist] = 7
else:
simp_district[dist] = int(dist[0])
In [45]:
race_num = {'ASIAN': 0,
'BLACK': 1,
'HISPANIC': 2,
'NATIVE AMERICAN': 3,
'OTHER': 4,
'WHITE': 5}
vt = {"Warning": 1,
"Citation": 2,
"ESERO": 3,
"SERO": 4}
In [46]:
data_reg = sam.copy()
data_reg["district_num"] = [simp_district[d] for d in data_reg["SubAgency"]]
#data_reg = pd.get_dummies(data_reg, columns = ["Race"])
#data_reg["Race_NATIVE"] = data_reg["Race_NATIVE AMERICAN"]
data_reg["race_num"] = [race_num[r] for r in data_reg["Race"]]
data_reg["violation_type_num"] = [vt[v] for v in data_reg["Violation Type"]]
data_reg["gender_num"] = [0 if g == "M" else 1 for g in data_reg["Gender"]]
data_reg["month"] = [m.month for m in sam["Date Of Stop"]]
data_reg["day"] = [d.day for d in sam["Date Of Stop"]]
data_reg
Out[46]:
In [47]:
#mlr = sm.ols(formula = 'lifeExp ~ year + continent_Asia + continent_Africa + continent_Europe + continent_Americas + continent_Oceania + year*continent_Asia + year*continent_Africa + year*continent_Europe + year*continent_Americas + year*continent_Oceania', data = data).fit()
distlr = sm.ols(formula = 'district_num ~ hour + race_num + gender_num + violation_type_num', data = data_reg).fit()
distlr.summary()
des = data_reg[["race_num", "gender_num", "violation_type_num"]].copy()
des["bias"] = 1
des = des[["bias","race_num", "gender_num", "violation_type_num"]]
dis = data_reg["district_num"]
X = np.asmatrix(des.to_numpy())
R = np.asmatrix(dis.to_numpy())
print (R)
A = np.linalg.inv(X.T * X) * X.T
W = (A * R.T).A1
W
Out[47]:
In [48]:
pred = W[0] + W[1]*des["race_num"] + W[2]*des["gender_num"] + W[3]*des["violation_type_num"]
pred
Out[48]:
In [49]:
sp = data_reg.sample(n = 500)
In [50]:
# Setting up the plot and dimension
fig, axs = plt.subplots(nrows = 1)
fig.set_figheight(15)
fig.set_figwidth(18)
predict = distlr.predict({"hour": sp["hour"],
"race_num": sp['race_num'], "gender_num": sp['gender_num'],
"violation_type_num": sp['violation_type_num']})
diff = sns.lineplot(x = predict, y =sp["district_num"], ax = axs)
plt.show()
predict
Out[50]:
In [ ]: